Recoil control of deepwater drilling riser systems via optimal control with feedforward mechanisms

Zhao, Yandong; Sun, Yue-Ting; Zhang, Bao-Lin; Han, Qing‐Long; Zhang, Xian-Ming

doi:10.1016/j.oceaneng.2022.111690

Cited by 17 publications

(35 citation statements)

References 26 publications

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“…No perturbation or uncertainty will be introduced in this case, and τ = 0 is employed in (23) and (24). The robust state feedback gain for system (6) is Simulation results are listed in Table 6, where ARHC outweighs SFOCLM in a lower control cost with similar vibration-attenuation performance. Specially, a non-adaption robust H ∞ controller (NRHC) is derived through Remark 3, which has no adaptive method.…”

Section: Figurementioning

confidence: 99%

“…When dealing with unknown and energy-bounded disturbances, as well as other sources of uncertainty mentioned above, it may be more practical to focus on finding feasible solutions with a high tolerance, rather than trying to derive an optimal solution directly [6]. To solve these uncertainty-induced problems, fuzzy-logic control and predictive networked control theories are utilized [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Active control theories are generally more effective than passive and semi‐active controllers in refraining the harmful response [3], including robust control method [4], sliding mode control strategy [5], and optimal control theory [6]. In the complex marine environment, however, external disturbances are not always periodic, accompanied by the inherited random characteristics of waves, currents, and winds [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive neural network H∞$H_\infty$ control for offshore platform with input delay and nonlinearity

Zhang,

Ma,

Wang

et al. 2023

IET Control Theory & Appl

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In this work, an adaptive learning robust controller is proposed to suppress the vibration of offshore platforms, which are subject to waves, winds, varying control delays and parametric perturbations. To realize nonlinear uncertainty approximation under the bounded performance, the controller incorporates both an online adaptive part and an offline fixed part. The adaptive part constructed by neural networks adjusts online, while the fixed part is obtained by regulating the performance. Importantly, adaptive updating strategy does not require accurate values or upper bounds for real‐time control delay or uncertainty. Several comparable experiments demonstrate the feasibility and effectiveness in vibration‐suppression of the designed adaptive controller in shallow/deep water. This scheme significantly reduces system response variations due to structural and hydrodynamic uncertainty, as well as additional random environmental forces caused by winds.

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive neural network H∞$H_\infty$ control for offshore platform with input delay and nonlinearity

Zhang,

Ma,

Wang

et al. 2023

IET Control Theory & Appl

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“…7 Resorting to the SFT or EPFT model, the friction force is represented by the output of a linear exosystem, which is further used to formulate the linear recoil control schemes in the literatures. 7,17 As such, the real-time approximation error between the friction force and the provided computational model has a negative impact on the recoil control performance directly, which implies the necessity of presenting a modeling method of friction force with high approximation accuracy. To obtain more satisfied recoil responses of the DDRS with friction force, a specialized nonlinear and robust recoil control scheme is demanded by effectively handling the friction force and the nonlinearities induced by tension force and actuator saturation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite‐time recoil control of deepwater drilling riser systems with nonlinear forces and saturation input using switched event‐triggered scheme

Cheng,

Zhang,

Zhu

2024

Intl J Robust & Nonlinear

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This article investigates the finite‐time recoil control problem of deepwater drilling riser systems with nonlinear friction force, tension force and saturation input. For the friction force, a new approximation model based on radial basis function neural networks is presented, which not only can ensure a better approximation effect than the sine‐function type and exponential‐polynomial‐function type computational models, but also can be easily used for control design. Different from the existing linear optimal control methods, a neural‐network‐based adaptive backstepping control method is proposed to deal with the nonlinear friction and tension forces, which can achieve better recoil control responses. An auxiliary system combining with the change of coordinates is employed to compensate the saturation input effect. To prolong the average release interval of control input while preserving satisfied control performance, a new switched event‐triggered control (ETC) scheme is developed, in which the triggering conditions are switched between the fixed and relative thresholds based on the strength of control signal. With the event‐triggered controller, the practical finite‐time stability condition of recoil control system is derived and the Zeno behavior is avoided. Numerical results are given to show the advantages of the proposed methods in handling the model nonlinearities, finite‐time stability and ETC performance of riser‐tension systems.

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“…Zhang et al [21] proposed an assessment method for the deepwater drilling riser under the influence of extreme sea conditions. Zhao et al [22] studied the dynamic model on the recoil control of the deepwater drilling riser system. Liu et al [23] proposed a deepwater drilling riser recoilcontrol method based on the control theory.…”

Section: Introductionmentioning

confidence: 99%

Influence of Auxiliary Pipelines of the Deepwater Drilling Riser on the Dynamic Characteristics of the Subsea Wellhead

Wang,

Gao

et al. 2023

JMSE

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During deepwater drilling, the subsea wellhead will be subjected to dynamic loads transmitted from the marine environment, floating drilling platform, riser, and blowout preventer (BOP). Therefore, complex dynamic responses will be induced, which will seriously affect the safety of the subsea wellhead. In this paper, considering the effect of auxiliary pipelines on the riser, a novel entire mechanical model of the floating platform–riser–BOP–subsea wellhead is established. By using the finite-difference method, the governing equations are solved. Finally, the dynamic bending moment and stress distribution of the subsea wellhead are obtained. Moreover, the model is verified by numerical simulation in Orcaflex. On this basis, the influence of the wave height, wave period, sludge height of the subsea wellhead, rotational stiffness of the lower flexible joint, and wall thickness of the conductor on the dynamic characteristics of the subsea wellhead is discussed. Analysis results show that the theoretical analysis results are in good agreement with the numerical simulation. The auxiliary pipelines have important influence on the dynamic characteristics of the subsea wellhead. Wave period is the most important factor affecting the mechanical behavior of the subsea wellhead. Wave height, wall thickness of the conductor, and sludge height are secondary factors affecting the mechanical behavior of the subsea wellhead. The rotational stiffness of the lower flexible joint has little influence on the mechanical behavior of the subsea wellhead. By solving the optimized mechanical model proposed in this paper, the dynamic characteristic of the subsea wellhead conforms more to the actual deepwater drilling conditions. This study has reference significance for the design and mechanical control of the subsea wellhead in deepwater drilling.

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Recoil control of deepwater drilling riser systems via optimal control with feedforward mechanisms

Cited by 17 publications

References 26 publications

Adaptive neural network H∞$H_\infty$ control for offshore platform with input delay and nonlinearity

Adaptive neural network H∞$H_\infty$ control for offshore platform with input delay and nonlinearity

Adaptive finite‐time recoil control of deepwater drilling riser systems with nonlinear forces and saturation input using switched event‐triggered scheme

Influence of Auxiliary Pipelines of the Deepwater Drilling Riser on the Dynamic Characteristics of the Subsea Wellhead

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